Episode 56 — Validate Third-Party Models: Assumptions, Limits, and Hidden Dependencies (Domain 3)
When using AI models developed by external vendors, the risk management challenge shifts from internal process control to external validation. This episode focuses on how to verify third-party models by probing their underlying assumptions, performance limits, and hidden dependencies on specific software libraries or data streams. For the AAIR certification, you must know how to ask the right questions during vendor assessments: How was the model trained? What are the known failure modes? Is the model's performance guaranteed under specific conditions? We discuss the danger of "vendor lock-in" and the importance of having a plan for model substitution if the third party fails or changes its service terms. Troubleshooting in this context involves identifying when a vendor’s "black box" model makes decisions that conflict with your organization’s internal ethics or risk policies. By conducting rigorous independent validation of third-party AI, risk professionals can treat these external components with the same level of scrutiny as internally developed systems. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
